Motion Vector Prediction Using Spatial-Temporal Candidate Patterns
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Solution Overview
Problem
Current video compression technologies face challenges in achieving a balance between compression efficiency and computational complexity, particularly in motion prediction, where existing methods struggle to accurately predict motion information across frames due to limitations in candidate selection and availability of motion data.
Innovation Solution
The proposed solution generates a list of motion vector candidates by combining spatial and temporal patterns, adjusting pattern positions based on the size of the current coding unit and grid size, and incorporating adjacent points to enhance motion prediction accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large set of potential prediction candidates is constructed from already encoded MVs, then motion prediction accuracy is improved, but device complexity and computational complexity increase
Solution Approach 1:
The patent segments the motion vector candidate selection process into multiple stages: first constructing a set of potential prediction candidates from already encoded MVs, then selectively refining this set based on specific criteria. This segmentation allows the system to manage the large set of candidates efficiently without processing all possibilities, thereby reducing computational complexity while maintaining prediction accuracy.
Solution Approach 2:
The patent performs preliminary actions by pre-constructing a set of potential prediction candidates from already encoded motion vectors before the actual motion prediction occurs. This preliminary candidate set is prepared in advance and can be refined selectively, reducing the computational burden during real-time prediction while ensuring accurate motion compensation.
2Productivity
If motion vectors are predictively coded using advanced MV prediction or merge mode, then compression efficiency is improved, but the availability and accuracy of motion data may be limited
Solution Approach 1:
The patent merges multiple sources of motion information by combining already encoded motion vectors from neighboring blocks with potential prediction candidates. This merging creates a more robust set of motion data that compensates for limitations in individual sources, ensuring both compression efficiency through predictive coding and reliability through diversified motion data availability.
3Adaptability or versatility
If pattern positions are adjusted based on current CU size and grid size, then motion prediction adapts to different coding units, but processing complexity increases
Solution Approach 1:
The patent implements dynamic adjustment of pattern positions based on the current coding unit size and grid size. Rather than using fixed patterns, the system adapts the candidate selection patterns dynamically to match the specific characteristics of each coding unit, improving versatility while managing processing complexity through systematic adaptation rules.
Data Source
AI summary
Motion vectors (MVs) are used as predictors for prediction of an image for a current coding unit (CU) within a current video frame. The MVs are from a list of motion vector candidates (MVCs), which is generated. The list includes MVs determined from a first or second pattern, each pattern specifying MVC positions. The first MV positions are within a current video frame, while the second MV positions are for a video frame different from the current frame. The MVC list is generated for a current CU with the MV positions being relative to a position of the current CU. In particular, the MVC list is generated in dependence on a size of the current CU and a size of a grid specifying a minimum distance between two MV positions. One or more MVs as specified by the MV positions of said pattern are included into the MVC list.